Monday, September 1, 2014

Waterfalls, this time with actual falling water

A few weeks ago I went to Jervis Bay with ANU lecturers and students for the third time. I always wanted to show the area to my family, and this weekend we finally managed to go camping there. My wife enjoyed the native plants and animals, my daughter loved the beach, and both admired the landscape.


On the way there, we stopped at Fitzroy Falls in the Moss Vale area. This impressive waterfall is 81 m deep, but as can be seen in the above picture there was quite a lot of fog so we could not see it under ideal conditions. This place is really wet most of the time, the vegetation is pretty much a temperate rainforest.


On the way back, we also stopped at the same place as I did with the student field trip - Tianjara Falls. Then I called it an alleged waterfall because it was dry, but this time we were rewarded with a much nicer view! For some time there was even a rainbow in the spray.


To end on a somewhat weirder note: The family posing in front of the Big Merino of Goulburn. These various big whatevers of Australia are of course greatly amusing to a five year old, but I wonder what archaeologists of the future are going to make of a megalithic sheep. If it can still be reconstructed after two thousand years it will probably be assigned some kind of religious significance. Then again, I doubt that a structure like this one will last as long as Roman masonry.

Thursday, August 28, 2014

Botany picture #172: Acacia decurrens


I am reasonably optimistic that this is indeed Acacia decurrens (Fabaceae), one of many wattles that are currently in flower here in Canberra. This was a small tree in Mount Majura Nature Reserve. It is the first time that I notice an Acacia with the styles of the individual flowers sticking out of the heads like that.

Wednesday, August 27, 2014

Final update on using fastStructure and similar software

After my somewhat mixed experience trying to use fastStructure, I have recently found the time to throw my data at two other programs for inferring population structure.

To recap, I have thousands of SNPs for two groups of species, in one case from 91 individuals and in the other from 224 individuals and I want to know how best to group the individuals into separate 'populations', in the present case potential species. I originally used fastStructure because it was new and supposedly written specifically for large numbers of SNPs, but the results were ultimately odd. The clusters didn't make very much sense and the program found virtually no admixed individuals, that is hybrids, although there really should have been some.

Earlier this week I then tried the R package adegenet. On the plus side, it turned out to be very simple and user-friendly. Of course you need to know how to use R, but the manual of the package is well written, and adegenet has a straightforward "read" function for importing datasets. It easily imported my Structure file without any hiccups, and after that it was a simple manner of handing my data over to adegenet's "find.clusters" function.

However, I tried different settings and did not get reasonable populations with any of them. One problem in my dataset are missing data, and I found that setting allele frequencies to zero for those cases produced the most meaningful results, but still there were several populations with no samples in them and the populations that had samples didn't make a lot of sense.

Yesterday I finally tried my luck with good old Structure itself - somewhat hesitatingly because I feared it would be very slow with such a big dataset. Yes, even for my smaller dataset what I wanted to do ran overnight, but that is still faster than I feared, and the results are worth it. The populations make sense, and in marked contrast to fastStructure it finds evidence of admixture. My larger dataset will probably need several days to be analysed, but if that is necessary so be it.

There is probably a reason why that program is the most popular in the area...

A new low in science spam

Science spammers sometimes use scripts to mine journals or article databases for authors' contact details and then generate automatic spam. That saves them a lot of work, of course, but one has to wonder about the efficiency of that approach because the results are so bizarre and off-putting.

Behold:
Dear [my name]; [name of co-author]; [name of co-author]; [name of co-author]; [name of co-author]; [name of senior author],
Once you published a paper titled [name of our paper] in [name of the journal] . With such attractive theme [what follows are the keywords of our article] Cell size; chromosome; flow cytometry; genome size; guard cell size; ploidy, the article is so outstanding. It shows your professional and rigorous attitude. On behalf of the academic world, we appreciate your contribution to the research filed [sic!] very much. And we wonder if you have any new progress or you are doing any new study about your interested field.
Science Publishing Group [link], who [sic!] publishes Journals, Special Issues, Books and Conference Proceedings, now sincerely invites you to contribute your new articles to the website.
[another link]
Submit Your Latest Research
New progress of your latest research
New study in your research field
A view for the new research trends
Please submit your new papers via:  [another link]
Advantages to Publish with SciencePG
Peer Review: Effective and professional
High Visibility: Up to 40,000 visitors per day
Open Access: Open to the public free of charge
Low APC: Article Processing Charge ranges from 70 to 270USD
Abstracting and Indexing: WorldCat, CrossRef, JournalSeek, CASSI, etc.
If you are doing some new study, please kindly notify us. And we are looking forward to your participation.
It is even worse because all the fields that were filled in by the script are in italics in the original (like the keywords above), making it even easier to see what is going on. So sad.

Monday, August 25, 2014

Categorical Analysis of Neo- and Paleo-Endemism

So, after laying the groundwork by looking into biodiversity metrics such as (Species) Richness, Corrected Weighted Endemism (CWE), Phylogenetic Diversity (PD) and Phylogenetic Endemism (PE), we can come to the first of two recent papers that I wanted to discuss. A few weeks ago several friends and colleagues, some of them from my own institution, published a new, quantitative method for locating hotspots of endemism called Categorical Analysis of Neo- and Paleo-Endemism (CANAPE; Mishler et al., 2014).

Saturday, August 23, 2014

Botany picture #171: Banksia coccinea


Banksia coccinea (Proteaceae), Western Australia, 2012. To my great surprise I found this species being sold as a cut-flower in our little local supermarket just yesterday. Because my wife likes the genus so much I bought one of them and it is now sitting on our dinner table.

Also, we are rather proud that our five year old daughter was able to immediately identify it as a Banksia although she had never seen this particular species before.

Wednesday, August 20, 2014

Diversity metrics

I want to blog about two recently published papers, one on keys and one on a method for spatial analyses of biodiversity, but for the latter some groundwork is necessary. This post will provide that groundwork so that I can then cunningly link back to it.

The last 25 years or so have seen the rise of spatial studies of patterns of biodiversity. They have been made possible by the increased availability of large databases with specimen occurrence records such as Australia's Virtual Herbarium, for example. Where a generation ago most information on the occurrence of species came from distribution maps drawn by specialists on the various groups of organisms, we can now enter a species name into a database search and are rewarded by a large list of geocoded specimens ready for use in our analyses.

Over the same time, several new diversity metrics have been developed to allow ever more sophisticated analyses. What is a diversity metric? It is a numerical value that tells us how diverse the organisms of our study group are in a particular part of our study area.

The study area as a whole is divided into cells; ideally these are equal area cells of for example 100 km x 100 km, alternatively they are biogeographical or political units. We can then look at our diversity metric and say, aha, in this cell there is particularly high diversity, and that might influence our decisions about what areas to prioritise for conservation. Okay, now what metrics are there?